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the scale will consistently measure the same weight every time. Other measures, including some
psychological tests, may be less reliable, and thus less useful.
Normally, we can assume that the researchers have done their best to assure the construct
validity of their measures, but it is not inappropriate for you, as an informed consumer of
research, to question this. It is always important to remember that the ability to learn about the
relationship between the conceptual variables in a research hypothesis is dependent on the
operational definitions of the measured variables. If the measures do not really measure the
conceptual variables that they are designed to assess (e.g., if a supposed IQ test does not really
measure intelligence), then they cannot be used to draw inferences about the relationship
between the conceptual variables (Nunnally, 1978). [2]
The statistical methods that scientists use to test their research hypotheses are based on
probability estimates. You will see statements in research reports indicating that the results were
“statistically significant” or “not statistically significant.” These statements will be accompanied
by statistical tests, often including statements such as “p < 0.05” or about confidence intervals.
These statements describe the statistical significance of the data that have been
collected. Statistical significance refers to the confidence with which a scientist can conclude that
data are not due to chance or random error. When a researcher concludes that a result is
statistically significant, he or she has determined that the observed data was very unlikely to have
been caused by chance factors alone. Hence, there is likely a real relationship between or among
the variables in the research design. Otherwise, the researcher concludes that the results were not
statistically significant.
Statistical conclusion validity refers to the extent to which we can be certain that the researcher
has drawn accurate conclusions about the statistical significance of the research. Research will
be invalid if the conclusions made about the research hypothesis are incorrect because statistical
inferences about the collected data are in error. These errors can occur either because the
scientist inappropriately infers that the data do support the research hypothesis when in fact they
are due to chance, or when the researcher mistakenly fails to find support for the research
hypothesis. Normally, we can assume that the researchers have done their best to ensure the
statistical conclusion validity of a research design, but we must always keep in mind that